Statistical programming

Creating graphics with ggplot2

Marc Comas-Cufí

Today’s session

ggplot2: a system for declaratively creating graphics, based on “The Grammar of Graphics”
ggplot2 extensions.

R plotting systems

graphics. Defaults R plotting system. Fast for exploratory analysis. Nice graphics are constructed step by step using different calls.
grid package based.
- lattice. Fast plots for exploratory analysis. By default plots are nicer than base system. Tuning is difficult.
- ggplot2. System implementing a layered grammar of graphics.

Visualising data with `ggplot2`

Fuel economy data from 1999 to 2008 for 38 popular models of cars:

library(tidyverse)
data(mpg, package='ggplot2')
mpg

# A tibble: 234 × 11
  manufacturer model displ  year   cyl trans   drv     cty   hwy fl   
  <chr>        <chr> <dbl> <int> <int> <chr>   <chr> <int> <int> <chr>
1 audi         a4      1.8  1999     4 auto(l… f        18    29 p    
2 audi         a4      1.8  1999     4 manual… f        21    29 p    
3 audi         a4      2    2008     4 manual… f        20    31 p    
4 audi         a4      2    2008     4 auto(a… f        21    30 p    
5 audi         a4      2.8  1999     6 auto(l… f        16    26 p    
# … with 229 more rows, and 1 more variable: class <chr>

Creating graphics with `ggplot2`

 ggplot(data = <DATA>) +                        # INITIAL LAYER
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>)) +  # NEXT LAYER
  ⋮
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>)) +  # LAST LAYER
  <TUNNING>

DATA: available variables
GEOM_FUNCTION: what should be plotted
MAPPINGS: relations between variables and aesthetics

Creating graphics with `ggplot2`

Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy))

Creating graphics with `ggplot2`

Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y, class \(\rightarrow\) color

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Creating graphics with `ggplot2`

Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y
We can fix the value of aesthetics

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), color = "#3366ff", shape = 15)

Creating graphics with `ggplot2`

Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y
We can add more layers

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(x = displ, y = hwy), method = 'lm')

Creating graphics with `ggplot2`

Mapping: displ \(\rightarrow\) x, hwy \(\rightarrow\) y
We can add more layers

ggplot(data = mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(method = 'lm')

Adding labels

ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point() +
  labs(title = "Bivariate plot",
       subtitle = "Relation between engine displacement and consumption",
       x = 'Engine displacement (liters)', y = 'Consumption (milles x gallon)', 
       color = 'Car class', caption = "Statistical Programming Course")

Themes

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth(method = 'lm') + 
  facet_grid(year~drv) +
  theme_minimal()

Themes

ggplot(mpg, aes(x = displ, y = hwy)) + 
  geom_point() + geom_smooth(method = 'lm') + 
  facet_grid(year~drv) +
  theme_minimal() +
  theme(strip.text = element_text(face = "bold.italic"), 
        strip.background = element_rect(fill = 'grey'))

Saving a `ggplot2`figure

# Check size with par('din')
ggsave(plot = p, filename =  "filename.pdf", width = 6, height = 4)
ggsave(plot = p, filename =  "filename.svg", width = 6, height = 4)

If plot parameter is omitted, last plot will be saved.

Learning more about `ggplot2`

https://ggplot2.tidyverse.org
Cheat sheet html
R Graphics Cookbook https://r-graphics.org/
The R Graph Gallery https://www.r-graph-gallery.com/ggplot2-package.html
gggplot2: Elegant Graphics for Data Analysis https://ggplot2-book.org/

Descriptive statistics: Univariate analysis

Summarising a categorical variable

How is the distribution of origin?

p1 = ggplot(data=flights) +
  geom_bar(aes(x = origin))

p2 = ggplot(data=flights) +
  geom_bar(aes(x = origin, 
               y=(..count..)/sum(..count..)))

library(patchwork)
p1 + p2

Summarising a categorical variable

dtab = flights %>% count(origin) %>%
  mutate(p = sprintf("%0.1f%%", 100*prop.table(n)),
         cn = rev(cumsum(rev(n))),
         y = cn + diff(c(cn,0))/2 )

ggplot(data=flights) +
  geom_bar(aes(x="",y=(..count..), fill=origin)) +
  geom_text(data=dtab, aes(x="", y=y, label = p)) +
  coord_polar(theta = 'y', start = pi/2, direction = 1) +
  theme_void()

Summarising a numerical variable

How is the distribution of dep_delay?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), bins = 10)

Summarising a numerical variable

How is the distribution of dep_delay?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), breaks = c(-50, 0, 50, 200, 1500))

Summarising a numerical variable

How is the distribution of dep_delay?

ggplot() +
  geom_boxplot(data=flights, aes(x=dep_delay))

Summarising a numerical variable

How is the distribution of dep_delay?

ggplot() +
  geom_density(data=flights, aes(x=dep_delay), col=NA, fill = 'blue', alpha=0.4)

Summarising a numerical variable

How is the distribution of dep_delay?

ggplot() +
  geom_density(data=flights, aes(x=dep_delay), col=NA, fill = 'blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60))

Summarising a numerical variable

How is the distribution of dep_delay?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), breaks=seq(-50,1500,5), 
                 fill='blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60))

Relation between numerical and categorical variables

How is the distribution of dep_delay and origin?

ggplot() +
  geom_boxplot(data=flights, aes(x=origin, y=dep_delay))

Relation between numerical and categorical variables

How is the distribution of dep_delay and origin?

ggplot() +
  geom_density(data=flights, aes(x=dep_delay), col=NA, fill = 'blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60)) +
  facet_wrap(~origin, ncol = 1)

Relation between numerical and categorical variables

How is the distribution of dep_delay and origin?

ggplot() +
  geom_histogram(data=flights, aes(x=dep_delay), breaks=seq(-50,1500,5), 
                 fill='blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60)) +
  facet_wrap(~origin, ncol = 1)

Relation between numerical and categorical variables

How is the distribution of dep_delay and origin?

library(ggridges)
ggplot() +
  geom_density_ridges(data=flights, aes(x=dep_delay, y = origin), 
                      scale=2, col=NA, fill = 'blue', alpha=0.4) +
  coord_cartesian(xlim = c(-30,60))

Relation between categorical variables

flights = flights %>% 
  filter(!is.na(arr_delay)) %>%
  mutate(arrival = if_else(arr_delay > 0, 'delayed', 'on-time'))

How is the distribution of origin and on.time

ggplot(data=flights) +
  geom_bar(aes(x=arrival, fill=origin))

Relation between categorical variables

flights_n = flights %>% count(origin, arrival)

origin relative frequencies

dplot = group_by(flights_n, arrival) %>% mutate(p = prop.table(n))
ggplot(data=dplot) +
  geom_bar(aes(x=arrival, y=p, fill=origin), stat = 'identity')

Relation between categorical variables

flights_n = flights %>% count(origin, arrival)

arrival relative frequencies

dplot = group_by(flights_n, origin) %>% mutate(p = prop.table(n))
ggplot(data=dplot) +
  geom_bar(aes(x=origin, y=p, fill=arrival), stat = 'identity')

Relation between numerical variables

How is the distribution of dep_delay and arr_delay?

ggplot(data=flights) +
  geom_point(aes(x=dep_delay, y=arr_delay))

Relation between numerical variables

ggplot(data=flights) +
  geom_point(aes(x=dep_delay, y=arr_delay, alpha = ..n..), size = 1, stat = 'sum')

# Equivalent,
# ggplot(data=count(flights, dep_delay, arr_delay)) +
#   geom_point(aes(x=dep_delay, y=arr_delay, alpha = n), size = 1, stat = 'identity')

`ggplot2` extensions

Packages with more themes

ggthemes. https://github.com/jrnold/ggthemes
ggtech. https://github.com/ricardo-bion/ggtech
ggthemr: https://github.com/cttobin/ggthemr
cowplot: https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html
bbplot. https://bbc.github.io/rcookbook
hrbrthemes: https://github.com/hrbrmstr/hrbrthemes

Interactive plots with `plotly`

library(plotly)

p = ggplot(data = mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(method = 'lm')

ggplotly(p)

Animations with `gganimate`

library(gganimate)
library(gapminder)

p <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
  geom_point(alpha = 0.7, show.legend = FALSE) +
  scale_colour_manual(values = country_colors) +
  scale_size(range = c(2, 12)) +
  scale_x_log10() +
  facet_wrap(~continent) +
  # Here comes the gganimate specific bits
  labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
  transition_time(year) +
  ease_aes('linear')

Animations with `gganimate`

animate(p, nframes = 20, fps = 5, width = 500, height=400)

3D plots with `rayshader`

https://www.tylermw.com/3d-ggplots-with-rayshader/

BUT REMEMBER:

Don’t use gratuitous 3D
It’s difficult to interpret static 3D visualizations

Other extensions

ggmap for maps https://github.com/dkahle/ggmap
ggternfor ternary diagrams http://www.ggtern.com/
ggdendro for dendrograms https://github.com/andrie/ggdendro

That’s all for today

Next week session

Overview of probability
- Probability
- Random variables
- The Central limit theorem
- Simulation

Statistical programming

Today’s session

R plotting systems

Visualising data with ggplot2

Creating graphics with ggplot2

Creating graphics with ggplot2

Creating graphics with ggplot2

Creating graphics with ggplot2

Creating graphics with ggplot2

Creating graphics with ggplot2

Adding labels

Faceting (facet_wrap() and facet_grid())

Faceting (facet_wrap() and facet_grid())

Themes

Themes

Saving a ggplot2figure

Learning more about ggplot2

Descriptive statistics: Univariate analysis

Summarising a categorical variable

Summarising a categorical variable

Summarising a numerical variable

Summarising a numerical variable

Summarising a numerical variable

Summarising a numerical variable

Summarising a numerical variable

Summarising a numerical variable

Relation between numerical and categorical variables

Relation between numerical and categorical variables

Relation between numerical and categorical variables

Relation between numerical and categorical variables

Relation between categorical variables

Relation between categorical variables

Relation between categorical variables

Relation between numerical variables

Relation between numerical variables

ggplot2 extensions

Packages with more themes

Interactive plots with plotly

Animations with gganimate

Animations with gganimate

3D plots with rayshader

Other extensions

That’s all for today

Next week session

Visualising data with `ggplot2`

Creating graphics with `ggplot2`

Creating graphics with `ggplot2`

Creating graphics with `ggplot2`

Creating graphics with `ggplot2`

Creating graphics with `ggplot2`

Creating graphics with `ggplot2`

Faceting (`facet_wrap()` and `facet_grid()`)

Faceting (`facet_wrap()` and `facet_grid()`)

Saving a `ggplot2`figure

Learning more about `ggplot2`

`ggplot2` extensions

Interactive plots with `plotly`

Animations with `gganimate`

Animations with `gganimate`

3D plots with `rayshader`